Instructions to use NourFakih/TimeSformer-GPT2-UCF-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NourFakih/TimeSformer-GPT2-UCF-mini with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="NourFakih/TimeSformer-GPT2-UCF-mini")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("NourFakih/TimeSformer-GPT2-UCF-mini")
model = AutoModelForImageTextToText.from_pretrained("NourFakih/TimeSformer-GPT2-UCF-mini")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NourFakih/TimeSformer-GPT2-UCF-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NourFakih/TimeSformer-GPT2-UCF-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NourFakih/TimeSformer-GPT2-UCF-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NourFakih/TimeSformer-GPT2-UCF-mini

SGLang

How to use NourFakih/TimeSformer-GPT2-UCF-mini with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NourFakih/TimeSformer-GPT2-UCF-mini" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NourFakih/TimeSformer-GPT2-UCF-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NourFakih/TimeSformer-GPT2-UCF-mini" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NourFakih/TimeSformer-GPT2-UCF-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NourFakih/TimeSformer-GPT2-UCF-mini with Docker Model Runner:
```
docker model run hf.co/NourFakih/TimeSformer-GPT2-UCF-mini
```

TimeSformer-GPT2-UCF-mini

This model is a fine-tuned version of Neleac/SpaceTimeGPT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3065
Rouge1: 23.736
Rouge2: 1.0376
Rougel: 18.1037
Rougelsum: 18.4236
Gen Len: 31.2877

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
0.1547	2.4876	500	0.3065	23.736	1.0376	18.1037	18.4236	31.2877

Framework versions

Transformers 4.51.1
Pytorch 2.5.1+cu124
Datasets 3.5.0
Tokenizers 0.21.0

Downloads last month: -

Safetensors

Model size

0.3B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for NourFakih/TimeSformer-GPT2-UCF-mini

Base model

facebook/timesformer-base-finetuned-k600

Finetuned

Neleac/SpaceTimeGPT

Finetuned

(3)

this model